This work considers active deanonymization of bipartite networks. The scenario arises naturally in evaluating privacy in various applications such as social networks, mobility networks, and medical databases. For instance, in active deanonymization of social networks, an anonymous victim is targeted by an attacker (e.g. the victim visits the attacker's website), and the attacker queries her group memberships (e.g. by querying the browser history) to deanonymize her. In this work, the fundamental limits of privacy, in terms of the minimum number of queries necessary for deanonymization, is investigated. A stochastic model is considered, where i) the bipartite network of group memberships is generated randomly, ii) the attacker has partial prior knowledge of the group memberships, and iii) it receives noisy responses to its real-time queries. The bipartite network is generated based on linear and sublinear preferential attachment, and the stochastic block model. The victim's identity is chosen randomly based on a distribution modeling the users' risk of being the victim (e.g. probability of visiting the website). An attack algorithm is proposed which builds upon techniques from communication with feedback, and its performance, in terms of expected number of queries, is analyzed. Simulation results are provided to verify the theoretical derivations.

中文翻译：

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主动攻击下双向网络中的基本隐私限制

这项工作考虑了双向网络的主动去匿名化。在评估各种应用程序（例如社交网络、移动网络和医疗数据库）中的隐私时，自然会出现这种情况。例如，在社交网络的主动去匿名化中，匿名受害者成为攻击者的目标（例如受害者访问攻击者的网站），并且攻击者查询她的组成员身份（例如通过查询浏览器历史记录）以去匿名化她。在这项工作中，就去匿名化所需的最少查询数量而言，研究了隐私的基本限制。考虑了一个随机模型，其中 i) 组成员的二分网络是随机生成的，ii) 攻击者对组成员具有部分先验知识，以及 iii) 它收到对其实时查询的嘈杂响应。二分网络是基于线性和次线性优先连接以及随机块模型生成的。受害者的身份是基于对用户成为受害者的风险（例如访问网站的概率）进行建模的分布而随机选择的。提出了一种攻击算法，该算法建立在与反馈通信的技术之上，并分析了其在预期查询数量方面的性能。提供仿真结果来验证理论推导。提出了一种攻击算法，该算法建立在与反馈通信的技术之上，并分析了其在预期查询数量方面的性能。提供仿真结果来验证理论推导。提出了一种攻击算法，该算法建立在与反馈通信的技术之上，并分析了其在预期查询数量方面的性能。提供仿真结果来验证理论推导。